Si Eun Kim1, Jin San Lee1, Sookyoung Woo1, Seonwoo Kim1, Hee Jin Kim1, Seongbeom Park1, Byung In Lee1, Jinse Park1, Yeshin Kim1, Hyemin Jang1, Seung Joo Kim1, Soo Hyun Cho1, Byungju Lee1, Samuel N Lockhart1, Duk L Na1, Sang Won Seo2. 1. From the Departments of Neurology (S.E.K., H.J.K., S.P., H.J., S.J.K., S.H.C., D.L.N., S.W.S.), Clinical Research Design and Evaluation (S.W.S.), and Health Sciences and Technology (D.L.N.), SAIHST, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul; Department of Neurology (S.E.K., B.I.L., J.P.), Inje University College of Medicine, Haeundae Paik Hospital, Busan; Department of Neurology (J.S.L.), Kyung Hee University Hospital; Statistics and Data Center (S.W., S.K.), Center for Clinical Epidemiology (S.W.S.), and Samsung Alzheimer Research Center, Neuroscience Center (H.J.K., S.P., H.J., D.L.N., S.W.S.), Samsung Medical Center, Seoul; Department of Neurology (Y.K.), Kangwon National University College of Medicine, Chuncheon-si, Gangwon-do; Department of Neurology (S.J.K.), Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital; Department of Neurology (S.H.C.), Chonnam National University Hospital, Gwangju; Department of Neurology (B.L.), Yuseong Geriatric Rehabilitation Hospital, Pohang, Korea; and Department of Internal Medicine (S.N.L.), Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC. 2. From the Departments of Neurology (S.E.K., H.J.K., S.P., H.J., S.J.K., S.H.C., D.L.N., S.W.S.), Clinical Research Design and Evaluation (S.W.S.), and Health Sciences and Technology (D.L.N.), SAIHST, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul; Department of Neurology (S.E.K., B.I.L., J.P.), Inje University College of Medicine, Haeundae Paik Hospital, Busan; Department of Neurology (J.S.L.), Kyung Hee University Hospital; Statistics and Data Center (S.W., S.K.), Center for Clinical Epidemiology (S.W.S.), and Samsung Alzheimer Research Center, Neuroscience Center (H.J.K., S.P., H.J., D.L.N., S.W.S.), Samsung Medical Center, Seoul; Department of Neurology (Y.K.), Kangwon National University College of Medicine, Chuncheon-si, Gangwon-do; Department of Neurology (S.J.K.), Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital; Department of Neurology (S.H.C.), Chonnam National University Hospital, Gwangju; Department of Neurology (B.L.), Yuseong Geriatric Rehabilitation Hospital, Pohang, Korea; and Department of Internal Medicine (S.N.L.), Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC. sangwonseo@empal.com.
Abstract
OBJECTIVE: To investigate whether cardiometabolic factors were associated with age-related differences in cortical thickness in relation to sex. METHODS: In this cross-sectional study, we enrolled 1,322 cognitively normal elderly (≥65 years old) individuals (774 [58.5%] men, 548 [41.5%] women). We measured cortical thickness using a surface-based analysis. We analyzed the associations of cardiometabolic risk factors with cortical thickness using multivariate linear regression models after adjusting for possible confounders and interactions with age. RESULT: Among women, hypertension (β = -1.119 to -0.024, p < 0.05) and diabetes mellitus (β = -0.920, p = 0.03) were independently associated with lower mean cortical thickness. In addition, there was an interaction effect between obesity (body mass index [BMI] ≥27.5 kg/m2) and age on cortical thickness in women (β = -0.324 to -0.010, p < 0.05), suggesting that age-related differences in cortical thickness were more prominent in obese women compared to women with normal weight. Moreover, low education level (<6 years) was correlated with lower mean cortical thickness (β = -0.053 to -0.046, p < 0.05). Conversely, among men, only being underweight (BMI ≤18.5 kg/m2, β = -2.656 to -0.073, p < 0.05) was associated with lower cortical thickness. CONCLUSIONS: Our findings suggest that cortical thickness is more vulnerable to cardiometabolic risk factors in women than in men. Therefore, sex-specific prevention strategies may be needed to protect against accelerated brain aging.
OBJECTIVE: To investigate whether cardiometabolic factors were associated with age-related differences in cortical thickness in relation to sex. METHODS: In this cross-sectional study, we enrolled 1,322 cognitively normal elderly (≥65 years old) individuals (774 [58.5%] men, 548 [41.5%] women). We measured cortical thickness using a surface-based analysis. We analyzed the associations of cardiometabolic risk factors with cortical thickness using multivariate linear regression models after adjusting for possible confounders and interactions with age. RESULT: Among women, hypertension (β = -1.119 to -0.024, p < 0.05) and diabetes mellitus (β = -0.920, p = 0.03) were independently associated with lower mean cortical thickness. In addition, there was an interaction effect between obesity (body mass index [BMI] ≥27.5 kg/m2) and age on cortical thickness in women (β = -0.324 to -0.010, p < 0.05), suggesting that age-related differences in cortical thickness were more prominent in obesewomen compared to women with normal weight. Moreover, low education level (<6 years) was correlated with lower mean cortical thickness (β = -0.053 to -0.046, p < 0.05). Conversely, among men, only being underweight (BMI ≤18.5 kg/m2, β = -2.656 to -0.073, p < 0.05) was associated with lower cortical thickness. CONCLUSIONS: Our findings suggest that cortical thickness is more vulnerable to cardiometabolic risk factors in women than in men. Therefore, sex-specific prevention strategies may be needed to protect against accelerated brain aging.
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